Cybernetics to Smart Grids: AI Feedback Loops That Work

Norbert Wiener published The Human Use of Human Beings in 1950. Seventy-five years later, one idea from that era still explains why so many “smart grid” programs either pay off quickly—or quietly disappoint: feedback.

Paul Jones’s recent poem (written as a rereading at 75) frames it beautifully: “Between humans and machines, feedback loops of love and grace.” It’s poetic, sure. But for utilities and energy operators, it’s also operationally literal. Grid automation, predictive maintenance, demand forecasting, and DER orchestration all live or die on feedback loops—measurement → decision → action → new measurement.

This post is part of our AI in Robotics & Automation series, where we usually talk about robots on factory floors or autonomous systems in warehouses. The power grid deserves to be in that conversation. It’s one of the largest robotic systems humans have ever built: sensors everywhere, actuators everywhere, and control logic increasingly driven by AI.

Cybernetics is the “control theory” behind energy AI

Cybernetics is about control and communication in animals and machines. That’s not a history lesson—it’s the blueprint for modern energy automation.

A utility’s AI program becomes “real” only when it closes loops:

• Sense: SCADA, PMUs, smart meters, line sensors, transformer monitors, battery telemetry
• Decide: forecasting models, anomaly detection, optimization, dispatch logic
• Act: switching, voltage regulation, DER curtailment, setpoints to inverters, truck rolls, automated work orders
• Verify: did the action reduce risk, cost, or outage minutes—or did it create a new problem?

Here’s my strong opinion: most AI projects in energy fail because they stop at “decide.” They build a model, publish a dashboard, and call it modernization. Cybernetics demands more: actions that change the world, plus instrumentation to prove those actions helped.

Why this matters more in 2025 than it did in 2015

As grids add inverter-based resources (solar, batteries, wind, HVDC, flexible loads), the system behaves less like a slow-moving machine and more like a fast, software-mediated one. Faster dynamics raise the stakes for automation. When the grid is software-shaped, bad feedback is worse than no feedback.

That’s where Wiener’s unease—“There is unease because of this. As there should be.”—lands for utilities. You want control. You also want humility.

Feedback loops show up everywhere utilities use AI

Energy AI isn’t one application; it’s a family of loops. The same cybernetic pattern repeats across grid operations.

Predictive maintenance: the loop that keeps assets alive

Predictive maintenance is often pitched as “AI that predicts failure.” The practical version is simpler and more valuable:

1. Instrument assets (transformers, breakers, cable circuits, rotating equipment, BESS)
2. Detect drift (temperature rise, dissolved gas patterns, partial discharge signatures, vibration changes)
3. Decide whether to act (inspect, repair, derate, replace)
4. Learn from outcomes (was the alert real? did the intervention work? what was the false-positive cost?)

That last step—learning from outcomes—is the missing link. Without it, you don’t have predictive maintenance. You have predictive noise.

Actionable move if you’re leading reliability:

• Require “closed-loop KPIs” for every AI maintenance model: precision/recall is fine, but also track avoidable outages, truck roll reduction, mean time between failure change, and maintenance backlog impact.

Demand forecasting: the loop that prevents expensive overreaction

Forecasting is another classic cybernetic loop:

• Forecast → dispatch/market position → actual demand → forecast error analysis → model update

Modern load patterns are harder: electrification, behind-the-meter solar, EV charging clusters, heat pumps, and extreme weather volatility. But the trap is timeless: operators stop trusting forecasts when the loop doesn’t explain itself.

Two practices I’ve found work when forecast trust is fragile:

• Separate “planning accuracy” from “operational safety.” A forecast can be directionally right but operationally risky in the tails.
• Show error drivers, not just error bars. Operators trust a model more when it says why it’s uncertain (holiday behavior, temperature swing, feeder topology change, DR event).

Grid optimization and volt/VAR: the loop that touches customers

Volt/VAR optimization (VVO) is where AI meets the real world fast—because actions change voltage and losses in minutes.

A healthy VVO loop looks like:

• Measure feeder conditions → compute setpoints → actuate regulators/cap banks/inverters → verify voltage compliance and losses → adjust

A fragile VVO loop looks like:

• Compute setpoints → actuate → discover the telemetry was stale → cause customer complaints → disable automation

If you’re deploying AI-driven VVO or DERMS controls, treat telemetry latency and data quality as first-class engineering work, not IT cleanup.

The grid is becoming a robotic system (and utilities should design it that way)

In the AI in Robotics & Automation world, we obsess over three things: sensing, actuation, and safety constraints. Utilities should borrow that discipline.

A modern distribution grid is an autonomous system with:

• Sensors: AMI, line monitors, fault indicators, BESS meters, inverter telemetry
• Actuators: switches, reclosers, regulators, capacitor banks, inverters, flexible loads
• Controllers: ADMS, DERMS, EMS, protection relays, edge controllers

The robotics lesson: automation without guardrails becomes expensive fast.

Practical guardrails for energy AI control loops

If you only take one section from this post, take this one.

Use these constraints to keep AI-based automation from creating operational surprises:

1. Bounded action space
   • AI suggests actions, but only within safe operating envelopes (thermal, voltage, protection coordination, interconnection constraints).
2. Fallback modes that are actually usable
   • Don’t design “manual fallback” that requires three experts and perfect data. Stress-test fallbacks during drills.
3. Rate limits and hysteresis
   • Feedback loops can oscillate. Add rate limits to setpoint changes and hysteresis to reduce “hunting.”
4. Human-in-the-loop thresholds
   • Define when the system is allowed to act autonomously (routine) and when it must request approval (novel conditions, high-impact actions).
5. Post-action verification
   • Every automated control action should trigger a check: did the grid respond as expected? If not, escalate or revert.

These are robotics basics. On the grid, they’re reliability basics.

Ethics in energy AI: Wiener’s warning fits critical infrastructure

Wiener was early in arguing that automation changes society, not just productivity. Utilities don’t have the luxury of treating ethics as branding. Energy is critical infrastructure. If AI behaves badly, people lose power, heat, medical device uptime, and trust.

Jones’s poem lands on a hard truth: “Control, yes, but rare freedom to some degree—freedom’s always a contingency.” In utilities, that translates to: we want automated control, but we must preserve meaningful human override and public accountability.

Three ethical failure modes utilities should actively design against

1) Automation bias in the control room

When systems look confident, humans defer—even when something feels off. Mitigation isn’t a training poster; it’s UI and process:

• Require “why this action” explanations
• Display counterfactuals (“if you don’t act, expected overload in X minutes”)
• Make uncertainty visible, not buried

2) Unequal reliability outcomes

AI-driven maintenance and automation can unintentionally prioritize already-healthy areas because the data is better and the assets are newer. Mitigate with:

• Equity-aware reliability metrics (SAIDI/SAIFI slices by region, feeder class, customer vulnerability)
• Data quality investment where visibility is worst

3) Model drift during rare events

Extreme weather, cyber incidents, and cascading failures are exactly when automation is most needed—and least likely to match training data.

Mitigation strategy that works in practice:

• Run “rare event playbooks” where AI switches to conservative policies (or decision support mode) under defined triggers.

A simple way to evaluate any AI program in a utility

When a vendor says “AI for grid optimization” or “AI for predictive maintenance,” ask this:

Where is the feedback loop closed, and what proves it improved outcomes?

If they can’t answer cleanly, you’re buying a demo.

Quick checklist (use this in your next AI steering meeting)

1. What’s the decision the AI changes? (dispatch, switching, maintenance prioritization, setpoints)
2. What actuator executes it? (system control, human workflow, automated work order)
3. What measurement confirms impact? (post-action telemetry, inspection results, outage metrics)
4. What’s the rollback plan? (revert setpoints, disable automation, alternate feeder configuration)
5. Who owns the loop end-to-end? (not “data science”—a named operator/engineering owner)

Utilities that do this well don’t treat AI as a project. They treat it as control engineering with accountability.

Next steps: build “friendship” between humans and machines

Jones ends with a line utilities should take seriously: “We are old enough to be friends. Let each kind be kind to the other.” Friendship, in operational terms, means humans and machines compensate for each other’s weaknesses.

• Machines are great at scanning millions of data points and responding in milliseconds.
• Humans are great at context, values, and recognizing when the situation is unlike the past.

If you’re leading AI in energy & utilities going into 2026 planning cycles, I’d focus less on “more models” and more on better loops: tighter measurement, safer actuation, clearer accountability, and ethics baked into operational design.

Where could your grid automation use a cleaner feedback loop right now—maintenance triage, voltage control, or DER coordination?